ArticleDer Radiologe2014
[Automatic segmentation and annotation in radiology].
Article in Der Radiologe, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The technical progress and broader indications for cross-sectional imaging continuously increase the number of radiological images to be assessed. However, as the amount of image information and available resources (radiologists) do not increase at the same pace and the standards of radiological interpretation and reporting remain consistently high, radiologists have to rely on computer-based support systems. Novel semantic technologies and software relying on structured ontological knowledge are able to "understand" text and image information and interconnect both. This allows complex database queries with both the input of text and image information to be accomplished. Furthermore, semantic software in combination with automatic detection and segmentation of organs and body regions facilitates personalized supportive information in topographical accordance and generates additional information, such as organ volumes. These technologies promise improvements in workflow; however, great efforts and close cooperation between developers and users still lie ahead.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.